The Predictive Ocean Atmosphere Model for Australia (POAMA) is a state-of-the-art seasonal to inter-annual seasonal forecast system based on a coupled ocean/atmosphere model and ocean/atmosphere/land observation assimilation systems.
The first version of POAMA (POAMA-1) was developed in a joint project involving the former Bureau of Meteorology Research Centre (BMRC) and former CSIRO Division of Marine Research, with support provided by the Climate Variability in Agriculture Program (CVAP), a consortium of rural research and development corporations managed by Land and Water Australia. The core of the research was carried out by scientists from the Oceanography Group at BMRC and scientist from the Oceans and Climate Group at CSIRO Marine Research. This first version went operational in October 2002 and produced routine forecasts of El Niño conditions.
Since then POAMA continues to be developed. A new version, POAMA-1.5, replaced POAMA-1 as the Bureau’s operational dynamical seasonal prediction system in September 2007. POAMA-2 (version P24) replaced POAMA-1.5 in 2011 and was the first operational pseudo multi-model system. Seasonal forecasts from this system are produced twice monthly.
Operational products consisting of various Sea Surface Temperature indexes for the Pacific and Indian oceans were made available from POAMA-2 (P24) on the National Climate Centres web site. Another set of operational products focussed on the prediction of extreme ocean temperature in the Great Barrier Reef (for warnings of coral bleaching) are available on the Bureau’s Oceanographic Services web site.
A new version of POAMA-2, version M24, has been developed as a seamless multi-week to seasonal forecast system. It is being run by Bureau Operations in trial mode and forms the basis for the new seamless experimental products on the POAMA research web site.
POAMA continues to be developed by scientists from the Centre for Australian Climate and Weather Research (CAWCR). New versions of the POAMA model are now based on the new national modelling system (ACCESS), which is used for all time scales from weather forecasting to climate change projections.^ Top
The new POAMA-2(M24) Seamless Multiweek to Seasonal Forecast System
A new version, POAMA-2 (version M24) is now being run by Bureau operations in trial mode, with the expectation that it will replace POAMA-2 (version P24) in 2013. Experimental products from this version form the basis for the experimental products on this web site.
Real-time forecasts from POAMA-2(M24) are produced every week by running a 33 member ensemble starting at 00Z each Thursday. Multi-week products are based on the latest 33 member ensemble. However, seasonal products are based on the last two weeks forecasts i.e. a 66 member ensemble, as this was found to give higher skill and reliability.
POAMA-2(M24) has several features that have led to significant improvements in skill on both multi-week and seasonal time scales. These include:
- A new state of the art assimilation system for using ocean observations
- A multi-model approach using three different configurations of the model (the 33-member ensemble is made up of 11 member ensembles from each model configuration).
- A new state of the art ensemble generation method that generates coupled bred vectors to initialise the forecasts (unique to M24 version)
- Some products utilise a lagged ensemble to increase the ensemble size to 66 and provide more reliability.
The real-time system is support by a comprehensive set of hind-casts using exactly the same system as used for real-time forecasts. This hind-cast set is used to calibrate the real-time forecasts and assess the skill of the forecast system. These consist of a 33-member ensemble starting on the 1st, 11th and 21st of each month from 1981 to 2011.
Experimental products are now available on the POAMA web site (http://poama.bom.gov.au). We are now working on transitioning some of these experimental products to Bureau operations.^ Top
Longer term future (POAMA3 and ACCESS)
Like the development of models for weather forecasting, the development of models for seasonal prediction is a long term activity with the development of sequential versions of the system. The core development of the main modules of the future versions of POAMA will be carried out in a project called ACCESS. ACCESS involves POAMA scientists as well as scientists from weather forecasting, climate change and ocean prediction. ACCESS brings together the modelling activities of CAWCR into the development of one common earth system model. POAMA-3 and beyond will completely based on the ACCESS model. This will involve a completely new atmospheric model on the atmospheric model developed by the UK Met Office. The ocean model will still be based on the GFDL MOM models, but will use their latest version (MOM4). The ACCESS model also includes a much more sophisticated land-surface models than currently used by POAMA, called CABLE and was developed at CSIRO. The observations assimilation system and ensemble generation strategy, which are used to initialise the forecasts will be based on extensions to the current system.^ Top
Why Coupled Models ?
The basis for seasonal prediction lies in variability driven by slow-processes in the climate system, particularly the ocean. Successful seasonal forecasts are often related to a model’s ability to reproduce and predict the slowly changing ocean state (e.g. associated with ENSO) and how this interacts with the atmosphere. The use of coupled atmosphere-ocean models for seasonal prediction is now commonplace in major international operational centres. These models couple the ocean and atmosphere and can use all the latest observations from ships, satellites, ground stations etc. to construct a picture of what the ocean, land and atmosphere look like today. A picture of how the state of the ocean, land and atmosphere is evolving is then generated using the coupled model. This model uses mathematical equations representing the laws of physics.
Unlike existing statistical forecasting systems, coupled models are not limited by historical relationships and can forecast a new set of climatic conditions. For example, because they simulate the real world they have the potential to predict how the impacts of one El Niño might be different to those of another.
One of the benefits of coupled models is that many forecasts (an ensemble) can be produced. If these are all close together then we can have confidence in the forecast. If they all differ significantly they can tell us that there is considerable uncertainty in the future and they give us the range of possibilities. Compared to weather forecasting, coupled model seasonal forecasting is still in its infancy. Still, great potential lies ahead.
By continuing this collaborative work and investing in further improving our system we will reap the full benefits of coupled models and provide Australia with a seasonals forecasting capability second to none.^ Top
Development and evaluation of the POAMA system has benefited from major external partnerships. These partnerships have contributed to both the development of POAMA/ACCESS and the evaluation of POAMA forecasts for particular applications. Our main partnerships are summarised below:
- Applying POAMA to agriculture in SW Western Australia, including enhancements to POAMA-2,
- Understanding skill and predictability on multi-week scales, including the development of new multi-week products,
- Understanding our ability to predict temperature extremes on multi-week time-scales, including the development of new extreme temperature prototype products,
- Understanding the teleconnections of Australian climate and how well they are simulated and predicted by POAMA,
- Evaluating how well POAMA can predict the Australian monsoon, including the development of prototype products.
- Understanding the skill in predicting the Indian Ocean, how much skill is limited by the initialisation strategy and investigating ways of improving the initialisation (e.g. through using new observing systems)
PCCSAP: this program supports the development of seasonal climate predictions using POAMA for some Pacific Island nations. This includes regional forecasts of temperature and rainfall, ocean surface temperature forecasts for marine applications e.g. coral bleaching and sea level forecasting.
GBRMPA: this collaboration supports the development of seasonal forecasts for coral bleaching risk for the Great Barrier Reef.
Tuna: this collaboration supports the creation of real time seasonal forecast products for the longline tuna fisheries on the east Australian coast.
Salmon: this project involves the development of real time POAMA forecast products for the salmon aquaculture industry in Tasmania.
Prawn: this project supports the development of real time multi-week and seasonal forecast products for the Queensland prawn industry.
WIRADA: this initiative supports the evaluation and application of seasonal climate predictions from POAMA for hydrological predictions throughout Australia.
South Eastern Australia Climate Initiative: this initiative supports research to evaluate POAMA rainfall forecasts for the SE of Australia, particularly in relation to supporting hydrological applications.
WAMSI: this initiative supports research to evaluate prediction and predictability of the large-scale drivers of variability of the Western Australian marine environment, particularly focusing on the impact of ENSO on the Leeuwin Current.^ Top